Convolutional Neural Network-based Automated Framework for Microscopy Analysis

Convolutional Neural Network-based Automated Framework for Microscopy Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 May 2025 V1 Latest version Share on Convolutional Neural Network-based Automated Framework for Microscopy Analysis Authors : Akshay Panchasara [email protected] , Om Borisagar , and Bharat Parmar Authors Info & Affiliations https://doi.org/10.22541/au.174822314.47857856/v1 209 views 128 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Microstructural characterization plays a crucial role in understanding materials properties by analyzing features such as pores, particles, grains, and grain boundaries in microscopy images. The use of such traditional techniques for such analysis is however often time consuming and tedious. Image data has become too large and complex to be interpreted by hand, which drives a growing need for computational models in microscopy analysis. Machine learning is rapidly changing the way image data are analyzed across biological and materials sciences. This work examines how machine learning methods can be integrated to improve existing steps in the microscopy analysis pipeline, from image classification and segmentation, which are often manually executed. In this research present automated system using YOLOv5 instance segmentation model developed for accurate and efficient for microscopy analysis. This work we use convolutional neural networks (CNNs) based image processing and deep learning segmentation models allow this method to automatically define and measure microscopic features, such as cells, particles, or defects, size and thickness, etc. In particular, our framework supports fast computational methods and is suitable to large data sets thus providing a wider range of applications and enhanced quantitative assessments. Supplementary Material File (ieee_for_journals_template_with_bibtex_example_files_included.pdf) Download 1.32 MB Information & Authors Information Version history V1 Version 1 26 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence convolutional neural networks machine learning scanning electron microscopy thin film Authors Affiliations Akshay Panchasara [email protected] Marwadi University View all articles by this author Om Borisagar Marwadi University View all articles by this author Bharat Parmar Marwadi University View all articles by this author Metrics & Citations Metrics Article Usage 209 views 128 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Akshay Panchasara, Om Borisagar, Bharat Parmar. Convolutional Neural Network-based Automated Framework for Microscopy Analysis. Authorea . 26 May 2025. 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